no code implementations • 29 Jan 2024 • Raphael Lafargue, Yassir Bendou, Bastien Pasdeloup, Jean-Philippe Diguet, Ian Reid, Vincent Gripon, Jack Valmadre
Fine-tuning is ineffective for few-shot learning, since the target dataset contains only a handful of examples.
1 code implementation • 24 Nov 2023 • Yassir Bendou, Vincent Gripon, Bastien Pasdeloup, Giulia Lioi, Lukas Mauch, Fabien Cardinaux, Ghouthi Boukli Hacene
In this paper, we present a novel approach that leverages text-derived statistics to predict the mean and covariance of the visual feature distribution for each class.
1 code implementation • 16 Jan 2023 • Yassir Bendou, Lucas Drumetz, Vincent Gripon, Giulia Lioi, Bastien Pasdeloup
Then, we introduce a downstream classifier meant to exploit the presence of multiple objects to improve the performance of few-shot classification, in the case of extreme settings where only one shot is given for its class.
1 code implementation • 13 Dec 2022 • Yassir Bendou, Vincent Gripon, Bastien Pasdeloup, Lukas Mauch, Stefan Uhlich, Fabien Cardinaux, Ghouthi Boukli Hacene, Javier Alonso Garcia
Such a set is hardly available in few-shot learning scenarios, a highly disregarded shortcoming in the field.
2 code implementations • 24 Jan 2022 • Yassir Bendou, Yuqing Hu, Raphael Lafargue, Giulia Lioi, Bastien Pasdeloup, Stéphane Pateux, Vincent Gripon
Few-shot learning aims at leveraging knowledge learned by one or more deep learning models, in order to obtain good classification performance on new problems, where only a few labeled samples per class are available.
Ranked #1 on Few-Shot Learning on Mini-Imagenet 5-way (1-shot)